Summary

Row

confirmed

2,364

death

8 (0.3%)

Row

Daily cumulative cases by type (Australia only)

Comparison

Column

Daily new cases

Cases distribution by type

Map

World map of cases (use + and - icons to zoom in/out)

About

The Coronavirus Dashboard: the case of Australia

This Coronavirus dashboard: the case of Australia provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for m. This dashboard is built with R using the R Makrdown framework and was adapted from this dashboard by Rami Krispin.

Code

The code behind this dashboard is available on GitHub.

Data

The input data for this dashboard is the dataset available from the {coronavirus} R package. Make sure to download the development version of the package to have the latest data:

install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")

The data and dashboard are refreshed on a daily basis.

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.

Contact

For any question or feedback, you can contact me. More information about this dashboard can be found in this article.

Update

The data is as of Wednesday March 25, 2020 and the dashboard has been updated on Thursday March 26, 2020.

---
title: "Coronavirus in Australia"
author: "Jason Everett"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
library(tidyverse)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
# data(coronavirus)
# update_datasets()
# View(coronavirus)
# max(coronavirus$date)

#----------------------------------------------------
# Pulling the coronvirus data from John Hopkins repo
# https://github.com/CSSEGISandData/COVID-19
#----------------------------------------------------
# Setting functions
# `%>%` <- magrittr::`%>%`
#----------------------------------------------------
#------------ Pulling confirmed cases------------
conf_url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
raw_conf <- read.csv(file = conf_url,
                     stringsAsFactors = FALSE)

lapply(1:ncol(raw_conf), function(i){
  if(all(is.na(raw_conf[, i]))){
    raw_conf <<- raw_conf[, -i]
    return(print(paste("Column", names(raw_conf)[i], "is missing", sep = " ")))
  } else {
    return(NULL)
  }
})

# Transforming the data from wide to long
# Creating new data frame
df_conf <- raw_conf[, 1:4]

for(i in 5:ncol(raw_conf)){
  
  raw_conf[,i] <- as.integer(raw_conf[,i])
  # raw_conf[,i] <- ifelse(is.na(raw_conf[, i]), 0 , raw_conf[, i])
  print(names(raw_conf)[i])
  
  if(i == 5){
    df_conf[[names(raw_conf)[i]]] <- raw_conf[, i]
  } else {
    df_conf[[names(raw_conf)[i]]] <- raw_conf[, i] - raw_conf[, i - 1]
  }
  
  
}


df_conf1 <- df_conf %>% 
  tidyr::pivot_longer(cols = dplyr::starts_with("X"),
                      names_to = "date_temp",
                      values_to = "cases_temp")

# Parsing the date
df_conf1$month <- sub("X", "",
                      strsplit(df_conf1$date_temp, split = "\\.") %>%
                        purrr::map_chr(~.x[1]) )

df_conf1$day <- strsplit(df_conf1$date_temp, split = "\\.") %>%
  purrr::map_chr(~.x[2])


df_conf1$date <- as.Date(paste("2020", df_conf1$month, df_conf1$day, sep = "-"))

# Aggregate the data to daily
df_conf2 <- df_conf1 %>%
  dplyr::group_by(Province.State, Country.Region, Lat, Long, date) %>%
  dplyr::summarise(cases = sum(cases_temp)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(type = "confirmed",
                Country.Region = trimws(Country.Region),
                Province.State = trimws(Province.State))

head(df_conf2)
tail(df_conf2)
#----------------------------------------------------
# Pulling death cases

death_url <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
raw_death <- read.csv(file =death_url,
                      stringsAsFactors = FALSE,
                      fill =FALSE)

lapply(1:ncol(raw_death), function(i){
  if(all(is.na(raw_death[, i]))){
    raw_death <<- raw_death[, -i]
    return(print(paste("Column", names(raw_death)[i], "is missing", sep = " ")))
  } else {
    return(NULL)
  }
})

# Transforming the data from wide to long
# Creating new data frame
df_death <- raw_death[, 1:4]

for(i in 5:ncol(raw_death)){
  print(i)
  raw_death[,i] <- as.integer(raw_death[,i])
  raw_death[,i] <- ifelse(is.na(raw_death[, i]), 0 , raw_death[, i])
  
  if(i == 5){
    df_death[[names(raw_death)[i]]] <- raw_death[, i]
  } else {
    df_death[[names(raw_death)[i]]] <- raw_death[, i] - raw_death[, i - 1]
  }
}


df_death1 <-  df_death %>% tidyr::pivot_longer(cols = dplyr::starts_with("X"),
                                               names_to = "date_temp",
                                               values_to = "cases_temp")

# Parsing the date
df_death1$month <- sub("X", "",
                       strsplit(df_death1$date_temp, split = "\\.") %>%
                         purrr::map_chr(~.x[1]) )

df_death1$day <- strsplit(df_death1$date_temp, split = "\\.") %>%
  purrr::map_chr(~.x[2])


df_death1$date <- as.Date(paste("2020", df_death1$month, df_death1$day, sep = "-"))

# Aggregate the data to daily
df_death2 <- df_death1 %>%
  dplyr::group_by(Province.State, Country.Region, Lat, Long, date) %>%
  dplyr::summarise(cases = sum(cases_temp)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(type = "death",
                Country.Region = trimws(Country.Region),
                Province.State = trimws(Province.State))

#---------------- Aggregate all cases ----------------

#
coronavirus <- dplyr::bind_rows(df_conf2, df_death2) %>%
  as.data.frame()


#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Australia") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "New Zealand", "NewZealand", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

df_daily <- coronavirus %>%
  dplyr::filter(Country.Region == "Australia") %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  #dplyr::mutate(active = confirmed - death - recovered) %>%
  dplyr::mutate(active = confirmed - death) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    # recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )


df1 <- coronavirus %>% dplyr::filter(date == max(date))
```

Summary
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```
















### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
                round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
                "%)",
                sep = ""
  ),
  caption = "Death cases (death rate)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


Row
-----------------------------------------------------------------------

### **Daily cumulative cases by type** (Australia only)

```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmed",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Death",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_annotations(
    x = as.Date(df_daily$date[df_daily$confirmed>0][1]),
    y = 1,
    text = paste("First case"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date(df_daily$date[df_daily$death>0][1]),
    y = 3,
    text = paste("First death"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -20,
    ay = -90
  ) %>%
  # plotly::add_annotations(
  #   x = as.Date("2020-03-23"),
  #   y = 100,
  #   text = paste(
  #     "New containment",
  #     "
", # "measures" # ), # xref = "x", # yref = "y", # arrowhead = 5, # arrowhead = 3, # arrowsize = 1, # showarrow = TRUE, # ax = -30, # ay = -90 # ) %>% # plotly::add_annotations( # x = as.Date("2020-03-25"), # y = 10, # text = paste( # "Stricter containment", # "
", # "measures" # ), # xref = "x", # yref = "y", # arrowhead = 5, # arrowhead = 3, # arrowsize = 1, # showarrow = TRUE, # ax = 10, # ay = -180 # ) %>% plotly::layout( title = "", yaxis = list(title = "Cumulative number of cases"), xaxis = list(title = "Date"), legend = list(x = 0.1, y = 0.9), hovermode = "compare" ) ``` Comparison ======================================================================= Column {data-width=400} ------------------------------------- ### **Daily new cases** ```{r} daily_confirmed <- coronavirus %>% dplyr::filter(type == "confirmed") %>% dplyr::filter(date >= "2020-02-29") %>% dplyr::mutate(country = Country.Region) %>% dplyr::mutate(country = dplyr::if_else(country == "New Zealand", "NewZealand", country)) %>% dplyr::filter(Country.Region == "Australia" | Country.Region == "New Zealand" | Country.Region == "China" | Country.Region == "US" | Country.Region == "Italy") %>% dplyr::group_by(date, country) %>% dplyr::summarise(total = sum(cases)) %>% dplyr::ungroup() %>% tidyr::pivot_wider(names_from = country, values_from = total) #---------------------------------------- # Plotting the data daily_confirmed %>% plotly::plot_ly() %>% plotly::add_trace( x = ~date, y = ~Australia, type = "scatter", mode = "lines+markers", name = "Australia" ) %>% plotly::add_trace( x = ~date, y = ~NewZealand, type = "scatter", mode = "lines+markers", name = "New Zealand" ) %>% plotly::add_trace( x = ~date, y = ~China, type = "scatter", mode = "lines+markers", name = "China" ) %>% plotly::add_trace( x = ~date, y = ~US, type = "scatter", mode = "lines+markers", name = "US" ) %>% plotly::add_trace( x = ~date, y = ~Italy, type = "scatter", mode = "lines+markers", name = "Italy" ) %>% plotly::layout( title = "", legend = list(x = 0.1, y = 0.9), yaxis = list(title = "Number of new cases"), xaxis = list(title = "Date"), # paper_bgcolor = "black", # plot_bgcolor = "black", # font = list(color = 'white'), hovermode = "compare", margin = list( # l = 60, # r = 40, b = 10, t = 10, pad = 2 ) ) ``` ### **Cases distribution by type** ```{r daily_summary} df_region <- coronavirus %>% # dplyr::filter(date == max(date)) %>% dplyr::filter(Country.Region == "Australia" | Country.Region == "New Zealand" | Country.Region == "China" | Country.Region == "US" | Country.Region == "Italy") %>% dplyr::group_by(Country.Region, type) %>% dplyr::summarise(total = sum(cases)) %>% tidyr::pivot_wider( names_from = type, values_from = total ) %>% # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>% dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>% dplyr::arrange(confirmed) %>% dplyr::ungroup() %>% dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>% dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>% dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>% dplyr::mutate(country = trimws(country)) %>% dplyr::mutate(country = factor(country, levels = country)) plotly::plot_ly( data = df_region, x = ~country, # y = ~unrecovered, y = ~ confirmed, # text = ~ confirmed, # textposition = 'auto', type = "bar", name = "Confirmed", marker = list(color = active_color) ) %>% plotly::add_trace( y = ~death, # text = ~ death, # textposition = 'auto', name = "Death", marker = list(color = death_color) ) %>% plotly::layout( barmode = "stack", yaxis = list(title = "Total cases"), xaxis = list(title = ""), hovermode = "compare", margin = list( # l = 60, # r = 40, b = 10, t = 10, pad = 2 ) ) ``` Map ======================================================================= ### **World map of cases** (*use + and - icons to zoom in/out*) ```{r} # map tab added by Art Steinmetz library(leaflet) library(leafpop) library(purrr) cv_data_for_plot <- coronavirus %>% # dplyr::filter(Country.Region == "m") %>% dplyr::filter(cases > 0) %>% dplyr::group_by(Country.Region, Province.State, Lat, Long, type) %>% dplyr::summarise(cases = sum(cases)) %>% dplyr::mutate(log_cases = 2 * log(cases)) %>% dplyr::ungroup() cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type) pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered")) map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner) names(cv_data_for_plot.split) %>% purrr::walk(function(df) { map_object <<- map_object %>% addCircleMarkers( data = cv_data_for_plot.split[[df]], lng = ~Long, lat = ~Lat, # label=~as.character(cases), color = ~ pal(type), stroke = FALSE, fillOpacity = 0.8, radius = ~log_cases, popup = leafpop::popupTable(cv_data_for_plot.split[[df]], feature.id = FALSE, row.numbers = FALSE, zcol = c("type", "cases", "Country.Region", "Province.State") ), group = df, # clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F), labelOptions = labelOptions( noHide = F, direction = "auto" ) ) }) map_object %>% addLayersControl( overlayGroups = names(cv_data_for_plot.split), options = layersControlOptions(collapsed = FALSE) ) ``` About ======================================================================= **The Coronavirus Dashboard: the case of Australia** This Coronavirus dashboard: the case of Australia provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for m. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin. **Code** The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}. **Data** The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} R package. Make sure to download the development version of the package to have the latest data: ``` install.packages("devtools") devtools::install_github("RamiKrispin/coronavirus") ``` The data and dashboard are refreshed on a daily basis. The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}. **Contact** For any question or feedback, you can [contact me](https://www.statsandr.com/contact/). More information about this dashboard can be found in this [article](https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/). **Update** The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.